Computationally-guided optimization of small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction

Structural basis for CEP192-mediated regulation of centrosomal AURKA

Aurora kinase A (AURKA) performs critical functions in mitosis. Thus, the activity and subcellular localization of AURKA are tightly regulated and depend on diverse factors including interactions with the multiple binding cofactors. How these different cofactors regulate AURKA to elicit different levels of activity at distinct subcellular locations and times is poorly understood. Here, we identified a conserved region of CEP192, the major cofactor of AURKA, that mediates the interaction with AURKA. Quantitative binding studies were performed to map the interactions of a conserved helix (Helix-1) within CEP192. The crystal structure of Helix-1 bound to AURKA revealed a distinct binding site that is different from other cofactor proteins such as TPX2. Inhibiting the interaction between Helix-1 and AURKA in cells led to the mitotic defects, demonstrating the importance of the interaction. Collectively, we revealed a structural basis for the CEP192-mediated AURKA regulation at the centrosome, which is distinct from TPX2-mediated regulation on the spindle microtubule.

Aurora Kinase A Regulation by Cysteine Oxidative Modification

Aurora kinase A (AURKA), which is a member of serine/threonine kinase family, plays a critical role in regulating mitosis. AURKA has drawn much attention as its dysregulation is critically associated with various cancers, leading to the development of AURKA inhibitors, a new class of anticancer drugs. As the spatiotemporal activity of AURKA critically depends on diverse intra- and inter-molecular factors, including its interaction with various protein cofactors and post-translational modifications, each of these pathways should be exploited for the development of a novel class of AURKA inhibitors other than ATP-competitive inhibitors. Several lines of evidence have recently shown that redox-active molecules can modify the cysteine residues located on the kinase domain of AURKA, thereby regulating its activity. In this review, we present the current understanding of how oxidative modifications of cysteine residues of AURKA, induced by redox-active molecules, structurally and functionally regulate AURKA and discuss their implications in the discovery of novel AURKA inhibitors.

The slow but steady rise of binding free energy calculations in drug discovery

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

A small-molecule inhibitor of the BRCA2-RAD51 interaction modulates RAD51 assembly and potentiates DNA damage-induced cell death

BRCA2 controls RAD51 recombinase during homologous DNA recombination (HDR) through eight evolutionarily conserved BRC repeats, which individually engage RAD51 via the motif Phe-x-x-Ala. Using structure-guided molecular design, templated on a monomeric thermostable chimera between human RAD51 and archaeal RadA, we identify CAM833, a 529 Da orthosteric inhibitor of RAD51:BRC with a K_d of 366 nM. The quinoline of CAM833 occupies a hotspot, the Phe-binding pocket on RAD51 and the methyl of the substituted α-methylbenzyl group occupies the Ala-binding pocket. In cells, CAM833 diminishes formation of damage-induced RAD51 nuclear foci; inhibits RAD51 molecular clustering, suppressing extended RAD51 filament assembly; potentiates cytotoxicity by ionizing radiation, augmenting 4N cell-cycle arrest and apoptotic cell death and works with poly-ADP ribose polymerase (PARP)1 inhibitors to suppress growth in BRCA2-wildtype cells. Thus, chemical inhibition of the protein-protein interaction between BRCA2 and RAD51 disrupts HDR and potentiates DNA damage-induced cell death, with implications for cancer therapy.

Exploitation of dihydroorotate dehydrogenase (DHODH) and p53 activation as therapeutic targets: A case study in polypharmacology

The tenovins are a frequently studied class of compounds capable of inhibiting sirtuin activity, which is thought to result in increased acetylation and protection of the tumor suppressor p53 from degradation. However, as we and other laboratories have shown previously, certain tenovins are also capable of inhibiting autophagic flux, demonstrating the ability of these compounds to engage with more than one target. In this study, we present two additional mechanisms by which tenovins are able to activate p53 and kill tumor cells in culture. These mechanisms are the inhibition of a key enzyme of the de novo pyrimidine synthesis pathway, dihydroorotate dehydrogenase (DHODH), and the blockage of uridine transport into cells. These findings hold a 3-fold significance: first, we demonstrate that tenovins, and perhaps other compounds that activate p53, may activate p53 by more than one mechanism; second, that work previously conducted with certain tenovins as SirT1 inhibitors should additionally be viewed through the lens of DHODH inhibition as this is a major contributor to the mechanism of action of the most widely used tenovins; and finally, that small changes in the structure of a small molecule can lead to a dramatic change in the target profile of the molecule even when the phenotypic readout remains static.

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein-ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman's rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.

A machine learning based intramolecular potential for a flexible organic molecule

Here, we employ the kernel regression machine learning technique to construct an analytical potential that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine.

Optimization of Protein-Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method

We present an explicit solvent alchemical free-energy method for optimizing the partial charges of a ligand to maximize the binding affinity with a receptor. This methodology can be applied to known ligand–protein complexes to determine an optimized set of ligand partial atomic changes. Three protein–ligand complexes have been optimized in this work: FXa, P38, and the androgen receptor. The sets of optimized charges can be used to identify design principles for chemical changes to the ligands which improve the binding affinity for all three systems. In this work, beneficial chemical mutations are generated from these principles and the resulting molecules tested using free-energy perturbation calculations. We show that three quarters of our chemical changes are predicted to improve the binding affinity, with an average improvement for the beneficial mutations of approximately 1 kcal/mol. In the cases where experimental data are available, the agreement between prediction and experiment is also good. The results demonstrate that charge optimization in explicit solvent is a useful tool for predicting beneficial chemical changes such as pyridinations, fluorinations, and oxygen to sulfur mutations.

Computational Fluorine Scanning Using Free-Energy Perturbation

We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to molecular dynamics simulations of a single compound and make predictions for the best binders out of numerous fluorinated analogues. We tested the method on nine test systems: renin, DPP4, menin, P38, factor Xa, CDK2, AKT, JAK2, and androgen receptor. The predictions were in excellent agreement with more rigorous alchemical free-energy calculations and in good agreement with experimental data for most of the test systems. However, the agreement with experiment was very poor in some of the test systems, and this highlights the need for improved force fields in addition to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chemistry to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addition to the available arsenal of free-energy methods.

Computation of protein-ligand binding free energies using quantum mechanical bespoke force fields

A quantum mechanical bespoke molecular mechanics force field is derived for the L99A mutant of T4 lysozyme and used to compute absolute binding free energies of six benzene analogs to the protein. Promising agreement between theory and experiment highlights the potential for future use of system-specific force fields in computer-aided drug design.

Study of the affinity between the protein kinase PKA and homoarginine-containing peptides derived from kemptide: Free energy perturbation (FEP) calculations

Protein kinases (PKs) discriminate between closely related sequences that contain serine, threonine, and/or tyrosine residues. Such specificity is defined by the amino acid sequence surrounding the phosphorylatable residue, so that it is possible to identify an optimal recognition motif (ORM) for each PK. The ORM for the protein kinase A (PKA), a well-known member of the PK family, is the sequence RRX(S/T)X, where arginines at the -3 and -2 positions play a key role with respect to the primed phosphorylation site. In this work, differential affinities of PKA for the peptide substrate Kemptide (LRRASLG) and mutants that substitute the arginine residues by the unnatural peptide homoarginine were evaluated through molecular dynamics (MD) and free energy perturbation (FEP) calculations. The FEP study for the homoarginine mutants required previous elaboration of a CHARMM "arginine to homoarginine" (R2B) hybrid topology file which is available in this manuscript as Supporting Information. Mutants substituting the arginine residues by alanine, lysine, and histidine were also considered in the comparison by using the same protocol. FEP calculations allowed estimating the free energy changes from the free PKA to PKA-substrate complex (ΔΔG_E→ES ) when Kemptide structure was mutated. Both ΔΔG_S→ES values for homoarginine mutants were predicted with a difference below 1 kcal/mol. In addition, FEP correctly predicted that all the studied mutations decrease the catalytic efficiency of Kemptide for PKA. © 2018 Wiley Periodicals, Inc.